Time Series Forecasting of Carbon Dioxide Concentration Based on Machine Learning Method

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Time series forecasting indicates LSTM outperforms Prophet in predicting daily carbon dioxide levels.

The LSTM model shows superior performance in predicting carbon dioxide concentrations compared to Prophet.
Descriptive statistical analysis identifies an upward trend and seasonal fluctuations in carbon dioxide data.
Time series forecasting employs two machine learning models—Prophet and LSTM, for daily-level predictions.
Insights gained may inform effective emission mitigation strategies amid ongoing climate change challenges.

Juyang Weng (2025) studied this question.

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View Full Paper

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Time series forecasting indicates LSTM outperforms Prophet in predicting daily carbon dioxide levels.

The LSTM model shows superior performance in predicting carbon dioxide concentrations compared to Prophet.
Descriptive statistical analysis identifies an upward trend and seasonal fluctuations in carbon dioxide data.
Time series forecasting employs two machine learning models—Prophet and LSTM, for daily-level predictions.
Insights gained may inform effective emission mitigation strategies amid ongoing climate change challenges.

Juyang Weng (2025) studied this question.

View Full Paper

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